Lead modeling strategy for content understanding (vision, NLP, multimodal), including architecture selection, training approach, and evaluation methodology.
Design and ship production models that generate content signals such as embeddings and classifications used across multiple product surfaces.
Own the full ML lifecycle: data/labeling strategy (human labels + weak supervision), training pipelines, offline evaluation, online experimentation, deployment, and monitoring/retraining.
Partner with infra/platform teams to ensure scalable, reliable training/serving (latency, cost, observability, rollout safety).
Collaborate with signal-consuming teams (ranking, retrieval, integrity, ads) to define signal contracts, adoption patterns, and success metrics.
Provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.

Requirements

M.S/ PhD degree in Computer Science, Statistics or related field.
Significant industry experience building software and ML pipelines/systems, including technical leadership (project/tech lead or equivalent).
Strong proficiency in Python and at least one ML stack such as PyTorch / TensorFlow, plus solid software engineering fundamentals.
Proven experience training and deploying ML models to production, including model versioning, rollouts, monitoring, and retraining strategies.
Deep hands-on experience in content understanding domains, such as:
computer vision (classification, detection, representation learning),
NLP (text classification, entity/topic modeling),
multimodal / embedding models (e.g., transformer-based representations).
Experience working with large-scale datasets and distributed compute (e.g., Spark-like ecosystems, distributed training, GPU environments).
Strong applied skills in evaluation and experimentation: defining metrics, offline/online alignment, A/B testing, debugging regressions, and model quality analysis.
Demonstrated ability to influence across teams and drive ambiguous problem areas to measurable outcomes.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonPyTorchTensorFlowML pipelinesmodel versioningA/B testingdebugging regressionsevaluation metricscontent understandingmultimodal models

Soft Skills

technical leadershipmentoringcollaborationinfluenceproblem-solving

Certifications

M.S. in Computer SciencePhD in Computer ScienceM.S. in StatisticsPhD in Statistics